Drive innovation and business transformation as a Custom Software Engineer, focusing on AI-first enterprise-grade solutions. This role involves designing, developing, and deploying solutions using Generative and Agentic AI frameworks.

We are seeking a professional with an AI-first mindset to leverage modern frameworks and agile practices. The goal is to deliver scalable, high-performing, and tailored solutions that address complex business challenges and automate processes.

This position emphasizes hands-on development and practical solution design for mid-level Application Architects and Senior Developers. A strong focus will be on developing and integrating intelligent insights into enterprise workflows for measurable impact.

Lead the design and delivery of AI-driven solutions, integrating Generative and Agentic AI to tackle complex business problems. Automate processes and embed intelligent insights into enterprise workflows.

Engage in hands-on coding, testing, and deployment of application-heavy solutions, writing efficient code in Java Spring Boot or Python, and contributing to front-end development with React or Angular.

Collaborate with senior architects on scalable, cloud-native application architectures across multi-cloud environments (AWS, Azure, Google Cloud). Build integrations using APIs, microservices, and event-driven patterns. Implement practical AI features and support containerized solutions with CI/CD pipelines.

Optimize applications for performance and security, adhering to best practices. Work closely with cross-functional teams to translate requirements into technical implementations and stay updated on full-stack development, cloud technologies, and AI advancements.

A minimum of 2-5 years of hands-on full-stack development experience is required, with at least 2 years in a lead developer or junior architect role. Demonstrated experience in multi-cloud environments (AWS, Azure, Google Cloud) is essential.

Proficiency in Java Spring Boot and Python for backend development is crucial. Hands-on experience with front-end frameworks such as React or Angular is also required. Basic exposure to AI/ML integration, using cloud AI services or simple ML frameworks, is necessary.

Practical experience with integration patterns (REST APIs, message queues) and microservices architecture is expected. Familiarity with containerization tools like Docker and Kubernetes, along with Infrastructure as Code (IaC) tools such as Terraform, is important.

Understanding of DevOps practices, CI/CD pipelines, monitoring tools, and secure coding standards is beneficial. Strong debugging and problem-solving skills, coupled with clear communication, are vital. An eagerness to learn new technologies is highly valued.